Fingerprint-based indoor localization is a critical enabling technology for Internet of Things (IoT) applications, where the primary challenges stem from complex environmental variability and prohibitive costs of data collection and labeling. This paper introduces a cross-domain multi-modal indoor localization framework that effectively combines visual and WiFi signals using few-shot learning techniques, achieving improved localization performance with minimal training data. We derive an upper bound on the generalized transfer localization error. Based on this bound, our learning-based approach applies feature-level knowledge distillation from pre-trained localization models. This process systematically calibrates discrepancies in feature distributions between the source and target environments. As a result, the proposed method significantly reduces the dependence on large labeled datasets. Experimental results demonstrate that our proposed method achieves substantial improvements over state-of-the-art localization models, with a mean localization error of 0.247 meters across diverse indoor environments, while requiring substantially fewer labeled samples in the target domain.

Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical structure. Traditional methods, such as pilot-based interpolation and compressive sensing, are not only computationally intensive but also lack adaptability. Current extrapolation techniques relying on rigid parametric models do not accommodate the dynamic environment of FAS, while data-driven deep learning approaches demand extensive training and are vulnerable to noise and hardware imperfections. To address these challenges, this paper introduces a novel self-supervised learning network (SSNet) designed for efficient and adaptive channel extrapolation in FAS. We formulate the problem of channel extrapolation in FAS as an image reconstruction task. Here, a limited number of unmasked pixels (representing the known CSI of the selected ports) are used to extrapolate the masked pixels (the CSI of unselected ports). SSNet capitalizes on the intrinsic structure of FAS channels, learning generalized representations from raw CSI data, thus reducing dependency on large labeled datasets. For enhanced feature extraction and noise resilience, we propose a mix-of-expert (MoE) module. In this setup, multiple feedforward neural networks (FFNs) operate in parallel. The outputs of the MoE module are combined using a weighted sum, determined by a gating function that computes the weights of each FFN using a softmax function. Extensive simulations validate the superiority of the proposed model. Results indicate that SSNet significantly outperforms benchmark models, such as AGMAE and long short-term memory (LSTM) networks by using a much smaller labeled dataset. A key observation is that the proposed model is more effectively trained using a small unmasked ratio of known CSI. Specifically, the proposed SSNet trained using CSI of 10 % total ports outperforms that trained using CSI of 25 % and 50 % total ports. This is because using a smaller number of known CSIs during training, the proposed model is forced to learn more effective channel correlation for channel extrapolation at the expense of higher training complexities. Ablation experiments reveal substantial performance gains from the MoE module’s integration. Furthermore, zero-shot learning experiments show a moderate performance degradation of about 3-5 dB, underscoring the model’s robust generalization ability. Finally, the inference speed experiments illustrate that the proposed model outperforms the benchmark models dramatically at the expense of a slightly longer execution time of 1.13 ms, 2.9 ms, and 3.12 ms on NVIDIA RTX 4090, 4060, and 3060 graphics processing units (GPU)s, respectively.

Integrated sensing and communication (ISAC) has emerged as a key enabler for next-generation wireless networks, supporting advanced applications such as high-precision localization and environment reconstruction. Cooperative ISAC (CoISAC) further enhances these capabilities by enabling multiple base stations (BSs) to jointly optimize communication and sensing performance through coordination. However, CoISAC beamforming design faces significant challenges due to system heterogeneity, large-scale problem complexity, and sensitivity to parameter estimation errors. Traditional deep learning-based techniques fail to exploit the unique structural characteristics of CoISAC systems, thereby limiting their ability to enhance system performance. To address these challenges, we propose a Link-Heterogeneous Graph Neural Network (LHGNN) for joint beamforming in CoISAC systems. Unlike conventional approaches, LHGNN models communication and sensing links as heterogeneous nodes and their interactions as edges, enabling the capture of the heterogeneous nature and intricate interactions of CoISAC systems. Furthermore, a graph attention mechanism is incorporated to dynamically adjust node and link importance, improving robustness to channel and position estimation errors. Numerical results demonstrate that the proposed attention-enhanced LHGNN achieves superior communication rates while maintaining sensing accuracy under power constraints. The proposed method also exhibits strong robustness to communication channel and position estimation error.

Accurate and robust localization is a critical enabler for emerging 5G and 6G applications, including autonomous driving, extended reality (XR), and smart manufacturing. While data-driven approaches have shown promise, most existing models require large amounts of labeled data and struggle to generalize across deployment scenarios and wireless configurations. To address these limitations, we propose a foundation-model-based solution tailored for wireless localization. We first analyze how different self-supervised learning (SSL) tasks acquire general-purpose and task-specific semantic features based on information bottleneck (IB) theory. Building on this foundation, we design a pretraining methodology for the proposed Large Wireless Localization Model (LWLM). Specifically, we propose an SSL framework that jointly optimizes three complementary objectives: (i) spatial-frequency masked channel modeling (SF-MCM), (ii) domain-transformation invariance (DTI), and (iii) position-invariant contrastive learning (PICL). These objectives jointly capture the underlying semantics of wireless channel from multiple perspectives. We further design lightweight decoders for key downstream tasks, including time-of-arrival (ToA) estimation, angle-of-arrival (AoA) estimation, single base station (BS) localization, and multiple BS localization. Comprehensive experimental results confirm that LWLM consistently surpasses both model-based and supervised learning baselines across all localization tasks. In particular, LWLM achieves 26.0%--87.5% improvement over transformer models without pretraining, and exhibits strong generalization under label-limited fine-tuning and unseen BS configurations, confirming its potential as a foundation model for wireless localization.

In this paper, we aim to improve the carbon efficiency (CE) of hybrid energy-supplied cellular networks by jointly optimizing communication and energy resources. The network is powered by both renewable and conventional grid energy. However, the stochastic and intermittent nature of renewable energy causes spatiotemporal mismatches between energy supply and traffic demand, thereby posing a challenge to CE improvement. Moreover, due to the nonlinearity of power amplifiers (PAs) at base stations (BSs), energy dissipation nonlinearly increases with transmit power. As a result, the existing static PA efficiency-based resource allocation may lead to energy inefficiency and CE degradation. On this basis, we formulate a stochastic long-term CE optimization problem that considers PA nonlinearity. Aided by Lyapunov optimization theory, the problem is equivalently transformed into three short-term deterministic subproblems, i.e., traffic flow control, resource allocation, and energy sharing. Leveraging this insight, we propose a queue-aware traffic flow control policy and a second-order cone programming-based resource allocation method to align traffic with PA characteristics. Additionally, a many-to-many stable matching-based energy sharing scheme is developed, where energy-deficient BSs are matched with energy-excessive BSs based on energy loss coefficients. Consequently, energy waste due to energy sharing is reduced, thereby improving CE.

Efficient power allocation and interference management are critical challenges in dynamic wireless communication systems. To address these challenges, graph neural networks (GNNs) have attracted significant attention, while knowledge graph further enhance this capability by representing structured interactions among entities. This article proposes the Power-focused Knowledge Graph Convolutional Network (PKGCN), a novel framework utilizing knowledge graph driven learning to model and optimize power allocation strategies. By integrating wireless-specific features such as channel conditions and interference metrics, PKGCN effectively captures the complex interactions and dependencies among network nodes. This model employs a message aggregation layer to extract local and global interactions and a power prediction layer to optimize resource allocation. Comprehensive evaluations reveal that PKGCN de-livers higher average user rates, lower interference levels, and greater robustness.

Multi-access edge computing (MEC) emerges as an effective computational paradigm. It meets the low latency and low energy consumption requirements of users by enabling user terminals (UE) to offload their computationally intensive applications to nearby access points (AP). However, the integration of Virtual Reality (VR) applications within MEC environments remains underexplored, primarily due to their structural complexity and high computational requirements for offloading and scheduling. To address these challenges, we use Directed Acyclic Graph (DAG) to model VR applications and 6G-oriented Rate-Splitting Multiple Access (RSMA) to enhance offloading and scheduling processes of VR applications. The objective is to jointly optimize the offloading strategy, transmit power, RSMA decoding order, and UE application scheduling to minimize the total system cost. Recognizing the limitations of traditional learning-based methods, which struggle with convergence in multi-user scenarios, we decompose the optimization problem into two subproblems: application offloading and application scheduling. We then propose the PPOCO algorithm, which integrates reinforcement learning with convex optimization to effectively solve these subproblems independently. Experimental results demonstrate that our proposed method consistently out-performs baseline algorithms in reducing the total system cost across various MEC network configurations.

For the Internet of things (IoT) network, the integrated localization and communication (ILAC) is expected to provide high localization and communication performance simultaneously. However, the existing research to evaluate the performance of ILAC systems fails to reveal the fundamental performance of ILAC systems in practical IoT network topology analytically. In this paper, we develop a unified analytical ILAC framework using stochastic geometry. We then validate the theoretical results obtained from the proposed analytical framework with the simulation results via extensive Monte Carlo simulations. We further analyse the communication coverage and localization coverage probability with respect to the network density, time-frequency-power domain resource allocation, and communication throughout and localization threshold. Finally, based on the ILAC simulation results, we reveal design guidance for ILAC systems. Specifically, we observe the fundamental trade-off between localization and communication performance attributed to the time-frequency-power domain resource allocation. Network density positively affects the ILAC performance, while power control is much less effective due to the dense network topology. The major observations are that time-domain (TD) resource allocation is preferred in dense networks with low localization CRB thresholds, while frequency-domain (FD) resource allocation dominates in sparse networks with large localization CRB thresholds.

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this article, we developed a distributed queue model for multitask deep neural network inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided proximal policy optimization algorithm, named LyaPPO. Through numerical results, we show that our LyaPPO algorithm outperforms the baseline algorithms. Specifically, under different maximum local computational capacities, the proposed algorithm decreases 24.29%–56.62% energy compared to the suboptimal baselines.

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

The emergence of 6th generation (6G) mobile networks brings new challenges in supporting high-mobility communications, particularly in addressing the issue of channel aging. While existing channel prediction methods offer improved accuracy at the expense of increased computational complexity, limiting their practical application in mobile networks. To address these challenges, we present LinFormer, an innovative channel prediction framework based on a scalable, all-linear, encoder-only Transformer model. Our approach, inspired by natural language processing (NLP) models such as BERT, adapts an encoder-only architecture specifically for channel prediction tasks. We propose replacing the computationally intensive attention mechanism commonly used in Transformers with a time-aware multi-layer perceptron (TMLP), significantly reducing computational demands. The inherent time awareness of TMLP module makes it particularly suitable for channel prediction tasks. We enhance LinFormer’s training process by employing a weighted mean squared error loss (WMSELoss) function and data augmentation techniques, leveraging larger, readily available communication datasets. Our approach achieves a substantial reduction in computational complexity while maintaining high prediction accuracy, making it more suitable for deployment in cost-effective base stations (BS). Comprehensive experiments using both simulated and measured data demonstrate that LinFormer outperforms existing methods across various mobility scenarios, offering a promising solution for future wireless communication systems.

This paper presents a novel two-stage deep reinforcement learning (DRL) algorithm built on a Transformer Encoder-based Deep Deterministic Policy Gradient (TEDDPG) framework, named TS-TEDDPG, which jointly optimizes the learning latency, energy consumption and model accuracy of Asynchronous Federated Learning (AFL) systems with prescribed security. The CPU configuration of local training and the transmit power of model uploading are learnt by the TEDDPG in the first stage. A linear programming-based device scheduling and cooperative jamming strategy is designed to efficiently optimize the rest of the decisions in the second stage and evaluates the immediate reward to train the TEDDPG. Experimental results based on a CNN model and the MNIST dataset demonstrate that the proposed TS-TEDDPG can reduce the training latency and energy consumption by 68.6% compared to its benchmarks, when the required test accuracy is 0.9.

In Network Slicing (NS), the hard RAN slicing interactions with the physical transmission environment have been explored predominantly, with limited consideration given to the interaction between slicing and existing transmission protocols. To address this gap, we propose a novel cross-slice re-transmission protocol integrated with NS, aiming to maximize throughput while ensuring latency constraints. Our protocol introduces adaptive re-transmission parameters, cross-slicing retransmission, and flexible duplexing mode switching. We model the problem as a bi-level optimization framework and propose a nested Hungarian-based reinforcement learning algorithm for optimization. Extensive experiments demonstrate the proposed protocol and algorithm's superiority in throughput and latency performance. Additionally, we investigate the impact of user count on throughput, providing configuration recommendations for optimal performance.

Because of the advantages of computation complexity compared with traditional localization algorithms, fingerprint based localization is getting increasing demand. Expanding the fingerprint database from the frequency domain by channel reconstruction can improve localization accuracy. However, in a mobility environment, the channel reconstruction accuracy is limited by the time-varying parameters. In this paper, we proposed a system to extract the time-varying parameters based on space-alternating generalized expectation maximization (SAGE) algorithm, then used variational auto-encoder (VAE) to reconstruct the channel state information on another channel. The proposed scheme is tested on the data generated by the deep-MIMO channel model. Mathematical analysis for the viability of our system is also shown in this paper.

The demand for high-precision localization con-tinues to grow rapidly with the development of information technology. Localization techniques based on wireless signals and visible light images have become the mainstream approach for achieving accurate and precise localization. However, directly utilizing multi-modal data for localization often overlooks the complex relationships between different modalities, particularly in terms of spatial and temporal features at varying scales. In this paper, we present a novel high-precision indoor localization method that effectively aligns the spatiotemporal dimensions of different modalities and extracts features efficiently using a shared neural network. To further enhance the extraction of relevant features from the multi-modal data, we propose a fusion network based on multi-modal channels that effectively minimize disparities, thereby significantly improving localization accuracy. Through extensive verification using a prototype system, our pro-posed solution demonstrates outstanding localization accuracy, achieving an average localization error of only 0.22m.

Free space optical (FSO) transmission has emerged as a key candidate technology for 6G to expand new spectrum and improve network capacity due to its advantages of large bandwidth, low-electromagnetic interference, and high-energy efficiency. Resonant beam operating in the infrared band utilizes spatially separated laser cavities to enable safe and mobile high-power energy and high-rate information transmission but is limited by Line-of-Sight (LoS) channel. In this article, we propose a reconfigurable intelligent surface (RIS) assisted resonant beam simultaneous wireless information and power transfer (SWIPT) system and establish an optical field propagation model to analyze the channel state information (CSI), in which LoS obstruction can be detected sensitively and non line-of-sight (NLoS) transmission can be realized by changing the phased of resonant beam in RIS. Numerical results demonstrate that, apart from the transmission distance, the NLoS performance depends on both the horizontal and vertical positions of RIS. The maximum NLoS energy efficiency can achieve 55% within a transfer distance of 10 m, a translation distance of ±4 mm, and rotation angle of ±50°.

In this article, we investigate the performance of reconfigurable intelligent surface (RIS)-aided spatial shift keying (SSK) wireless communication systems with imperfect channel state information (CSI). Specifically, we study the average bit error probability (ABEP) of two RIS-SSK systems based on intelligent reflection and blind reflection modes. For the intelligent RIS-SSK scheme, we first derive the conditional pairwise error probability of the composite channel through maximum-likelihood (ML) detection. Subsequently, we derive the probability density function of the combined channel. Due to the intricacies of the composite channel formulation, an exact closed-form ABEP expression is unattainable through direct derivation. To this end, we resort to employing the Gaussian–Chebyshev quadrature method to estimate the results. Additionally, we employ$Q$-function approximation to derive the nonexact closed-form expression in the presence of channel estimation errors. For the blind RIS-SSK scheme, we derive both closed-form ABEP expression and asymptotic ABEP expression with imperfect CSI by adopting the ML detector. To offer deeper insights, we explore the impact of discrete reflection phase shifts on the performance of the RIS-SSK system. Finally, we extensively validate all the analytical derivations via Monte Carlo simulations.

In order to provide design guidelines for energy efficient 6G networks, we propose a novel radial basis function (RBF) based optimization framework to maximize the integrated relative energy efficiency (IREE) metric. Different from the conventional energy efficient optimization schemes, we maximize the transformed utility for any given IREE using spectrum efficiency oriented RBF network and gradually update the IREE metric using proposed Dinkelbach’s algorithm. The existence and uniqueness properties of RBF networks are provided, and the convergence conditions of the entire framework are discussed as well. Through some numerical experiments, we show that the proposed IREE outperforms many existing SE or EE oriented designs and find a new Jensen-Shannon (JS) divergence constrained region, which behaves differently from the conventional EE-SE region. Meanwhile, by studying IREE-SE trade-offs under different traffic requirements, we suggest that network operators shall spend more efforts to balance the distributions of traffic demands and network capacities in order to improve the IREE performance, especially when the spatial variations of the traffic distribution are significant.

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This paper presents a new two-phase deep deterministic policy gradient (DDPG) framework, referred to as “TP-DDPG”, to balance online the learning delay and model accuracy of an FL process in an energy harvesting-powered HFL system. The key idea is that we divide optimization decisions into two groups, and employ DDPG to learn one group in the first phase, while interpreting the other group as part of the environment to provide rewards for training the DDPG in the second phase. Specifically, the DDPG learns the selection of participating clients, and their CPU configurations and the transmission powers. A new straggler-aware client association and bandwidth allocation (SCABA) algorithm efficiently optimizes the other decisions and evaluates the reward for the DDPG. Experiments demonstrate that with substantially reduced number of learnable parameters, the TP-DDPG can quickly converge to effective polices that can shorten the training time of HFL by 39.4% compared to its benchmarks, when the required test accuracy of HFL is 0.9.

This paper studies the fair transmission design for an intelligent reflecting surface (IRS) aided rate-splitting multiple access (RSMA). IRS is used to establish a good signal propagation environment and enhance the RSMA transmission performance. The fair rate adaption problem is constructed as a max-min optimization problem. To solve the optimization problem, we adopt an alternative optimization (AO) algorithm to optimize the power allocation, beamforming, and decoding order, respectively. A generalized power iteration (GPI) method is proposed to optimize the receive beamforming, which can improve the minimum rate of devices and reduce the optimization complexity. At the base station (BS), a successive group decoding (SGD) algorithm is proposed to tackle the uplink signal estimation, which trades off the fairness and complexity of decoding. At the same time, we also consider robust communication with imperfect channel state information at the transmitter (CSIT), which studies robust optimization by using lower bound expressions on the expected data rates. Extensive numerical results show that the proposed optimization algorithm can significantly improve the performance of fairness. It also provides reliable results for uplink communication with imperfect CSIT.

Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users’ quality of experience (QoE), we design a cross-layer transmission framework for real-time XR video. The proposed framework allows the simple information exchange between the base station (BS) and the XR server, which assists in adaptive bitrate and wireless resource scheduling. We utilize the cross-layer information to formulate the problem of maximizing user QoE by finding the optimal scheduling and bitrate adjustment strategies. To address the issue of mismatched time scales between two strategies, we decouple the original problem and solve them individually using a multi-agent-based approach. Specifically, we propose the multi-step Deep Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource scheduling strategy and then propose the Transformer-based Proximal Policy Optimization (TPPO) algorithm for video bitrate adaptation. The experimental results show that the TPPO+MS-DQN algorithm proposed in this study can improve the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm enhances the transmission quality by 49.9%-80.2%.

Intelligent reflecting surfaces (IRSs) have recently attained growing popularity in wireless networks owning to their capability to customize the wireless channel via smartly configured passive reflections. In addition to optimizing IRS reflection patterns, the flexible deployment of IRSs offers another design degree of freedom (DoF) to reconfigure the wireless propagation environment in favour of signal transmission. To unveil the impact of IRS deployment on the system capacity, we investigate the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements. In particular, both the distributed and centralized IRS deployment architectures are considered. Regarding the distributed IRS, the N IRS elements form multiple IRSs and each of them is installed near a user cluster; while for the centralized IRS, all IRS elements are located in the vicinity of the BS. To draw essential insights, we first derive the maximum capacity achieved by the distributed IRS and centralized IRS, respectively, under the assumption of line-of-sight (LoS) propagation and homogeneous channel setups. By carefully capturing the fundamental tradeoff between the spatial multiplexing gain and passive beamforming gain, we rigourously prove that the capacity of the distributed IRS is higher than that of the centralized IRS provided that the total number of IRS elements is above a threshold. Motivated by the superiority of the distributed IRS, we then focus on the transmission and element allocation design under the distributed IRS. By exploiting the user channel correlation of intra-clusters and inter-clusters, an efficient hybrid multiple access scheme relying on both spatial and time domains is proposed to fully exploit both the passive beamforming gain and spatial DoF. Moreover, the IRS element allocation problem is investigated for the objectives of the sum-rate maximization and the minimum user rate maximization, respectively. Finally, extensive numerical results are provided to validate our theoretical finding and also to unveil the effectiveness of the distributed IRS for improving the system capacity under various system setups.

Channel estimation is crucial to modern wireless systems and becomes increasingly challenging when the ultra-sized antenna is configured in sub-6GHz wireless communication systems. In an ultra-massive multiple-input multiple-output (U-MIMO) orthogonal frequency division multiplex (OFDM) system, the channel demonstrates spatial non-stationarity. Additionally, the limited pilot location in the OFDM system further complicates the channel estimation process. In this paper, we propose a model-data dual-driven (MDD) scheme to jointly perform the model-driven non-stationary channel denoising and the data-driven channel interpolation in an end-to-end way, which is followed by a low-complexity channel refinement module to improve the robustness of the proposed scheme. Specifically, image contour extraction (ICE) is utilized to effectively eliminate the non-stationary noises in the channel matrices before being sent to the downstream interpolation network. An enhanced convolutional neural network (CNN)-based residual network (eCNN-RN) is developed to perform non-linear interpolations for recovering the U-MIMO-OFDM channels. Based on ICE, the proposed online refinement module can improve the generalizability of the learned model to a practical environment. Numerical experiments demonstrate the efficiency and the effectiveness of the cross-fertilization of the model-driven and data-driven approaches.

Multiple input multiple output (MIMO) beamforming has been recognized as a key element to support challenging requirements in the vehicular communication environment. In order to provide accurate beam alignment and tracking results in the high mobility scenario, the mobility induced channel prediction mechanism has been proposed in the line-of-sight (LOS) fading environment, while the application to the practical mixed LOS and non-LOS fading environment is still open. In this paper, we propose a novel mobility and channel prediction combined beamforming (MCPCB) scheme to deal with this issue. Specifically, we rely on per-cluster angular based information and non-linear tracking scheme for angular-delay profile to obtain reliable single input multiple output (SIMO) channel prediction. By linking the estimated mobility parameters and the per-cluster beam directions, our proposed MCPCB scheme is able to provide a higher receiving energy with reduced prediction errors, and achieve more robust prediction performance when the cluster level channel blocking happens. Through analytical and numerical results, we show that the proposed MCPCB scheme can achieve about −50.6 dB and −87.7 dB of average received power gain in the LOS and NLOS scenarios, respectively, and outperform many conventional channel prediction methods.

Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home. Traditional model-based localization suffers from significant computational overhead, so fingerprint localization is getting increasing attention, which needs lower computation cost after the fingerprint database is built. However, the accuracy of indoor localization is limited by the complicated indoor environment which brings the multipath signal refraction. In this paper, we provided a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain by predicting the channel state information (CSI) values from another transmitting channel and spliced the multi-band information together to get more precise localization results. We tested our proposed scheme on COST 2100 simulation data and real time orthogonal frequency division multiplexing (OFDM) WiFi data collected from an office scenario.

The advent of online video services has resulted in a remarkable surge in Internet traffic, prompting the need for mobile edge computing (MEC) as a crucial element in augmenting the quality of adaptive streaming media services amidst the time-varying wireless channels. MEC reduces network backhaul traffic by providing video transcoding and adaptive streaming services closer to users. Nonetheless, the process of video transcoding introduces additional latency and energy consumption. In order to effectively tackle this challenge and uphold the optimal quality of experience (QoE), we propose the Joint Bitrate Transcoding and Parallel Cooperative Transmission (JBTPCT) model, which operates at the edge of mobile networks and handles multiple video chunks simultaneously. Within the JBTPCT model, the Asynchronous Advantage Actor-Critic (A3C) algorithm framework is employed to jointly account for radio access network conditions and MEC resources, leveraging a parallel execution strategy for transmission and transcoding. This integrated approach aims to minimize both latency and energy consumption while enhancing the QoE of video streaming. We evaluate the average QoE of JBTPCT in different network scenarios, and the experimental results demonstrate that JBTPCT consistently achieves higher average QoE compared to competing algorithms.

Smartphone based indoor localization has been widely explored for meeting the demand of high-precision low-cost indoor localization. Previous methods focus mainly on improving the localization accuracy of single sensor based localization, which may hinder their applications. In this article, we propose a novel encoder-decoder architecture for high-precision low-cost indoor localization. We first leverage two modal-specific encoders for feature extraction. Then, we propose two feature-level fusion strategies for feature fusion. Finally, we leverage two task-specific decoders for both position and orientation prediction. During training, we adopt WiFi-aided learning to provide a more reliable label. We test the proposed method in the corridor environment of a typical building. Experiments results show that our method can achieve less than a half meter localization accuracy, and meanwhile enjoys the run-time efficiency.

Cooperative inference in Mobile Edge Computing (MEC), achieved by deploying partitioned Deep Neural Network (DNN) models between resource-constrained user equipments (UEs) and edge servers (ESs), has emerged as a promising paradigm. Firstly, we consider scenarios of continuous Artificial Intelligence (AI) task arrivals, like the object detection for video streams, and utilize a serial queuing model for the accurate evaluation of End-to-End (E2E) delay in cooperative edge inference. Secondly, to enhance the long-term performance of inference systems, we formulate a multi-slot stochastic E2E delay optimization problem that jointly considers model partitioning and multi-dimensional resource allocation. Finally, to solve this problem, we introduce a Lyapunov-guided Multi-Dimensional Optimization algorithm (LyMDO) that decouples the original problem into per-slot deterministic problems, where Deep Reinforcement Learning (DRL) and convex optimization are used for joint optimization of partitioning decisions and complementary resource allocation. Simulation results show that our approach effectively improves E2E delay while balancing long-term resource constraints.

As a core performance metric for green communications, the conventional energy efficiency (EE) definition has successfully resolved many issues in the energy-efficient wireless network design. In the past several generations of wireless communication networks, the traditional EE measure plays an important role to guide many energy-saving techniques for slow varying traffic profiles. However, for the next-generation wireless networks, the traditional EE fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultrascale multiple antennas and space–air–ground integrated network (SAGIN). In this article, we present a novel EE metric named integrated relative EE (IREE), which is able to jointly measure the traffic profiles and the network capacities from the EE perspective. On top of that, the IREE-based green tradeoffs have been investigated and compared with the conventional energy-efficient design. Moreover, we apply the IREE-based green tradeoffs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and SAGIN. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional EE metric. Since the IREE-oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy-efficient network design.

Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as “TS-DQN,” for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

The sensing and communication layers are both integral parts of the Internet-of-Things. Most of recent studies on sensory status update treat the information sensing and sensory data communication problems separately (i.e., a decoupled approach) and optimize specific latency metrics such as age of information relying on simplified models of communication networks or sensory traffic. In this paper, we propose a deeply integrated sensing and communication scheduling (S2) framework based on status-error-triggered update, focusing specifically on multiaccess wireless networks. We first analyze a motivating example consisting of two-state Markov sensors, showing that when both optimized, S2 outperforms the decoupled approach significantly. For sensors with random-walk state transitions, the closed-form Whittle's index with arbitrary status tracking error functions is presented and the indexability is established. Furthermore, a mean-field approach is applied such that the decentralized status update medium access control design is solved explicitly, for both homogeneous nodes and heterogeneous nodes in terms of status transition behaviors. According to the numerical results, the performance of the proposed S2 scheme is close to the optimum and better than the decoupled approach. In addition, a potential application of dynamic Channel State Information (CSI) update is presented, with CSI generated by a commercial ray-tracing simulator.

Pre-distortion is a key technique to compensate for the nonlinear distortions caused by the transmitter in wireless communication systems. Generally, pre-distortion can be classified into digital pre-distortion (DPD) and analog pre-distortion (APD), which focus on optimizing and assessing the nonlinearity in their own areas. In this paper, we propose a new DPD approach to optimize the performance metric of the analog RF-domain (i.e., inter-modulation distortion (IMD) or adjacent channel power ratio (ACPR)) and that of the digital IF-domain (i.e., mean square error (MSE)) simultaneously. To make the joint design feasible, we derive a new hybrid performance metric, where the analog preferred metric is defined in the form of digital signals to bridge the gap between digital and analog signal processing. On top of that, an effective DPD scheme is developed based on a new dual time-delayed neural network (TDNN) learning architecture. The coefficients of the TDNN for power amplifier (PA) modeling can be trained offline with a PA dataset, while those of the TDNN for pre-distortion are obtained adaptively by optimizing the proposed joint design metric. Experimental results show that the proposed scheme is able to significantly improve the IMD/ACPR performance without compromising the MSE, compared to conventional DPD schemes.

The acquisition of channel state information (CSI) is a vital and challenging task in wireless communications, especially for high mobility scenarios with rapidly varying channels. Channel prediction, as an effective approach to acquire CSI, is considered a promising approach to improve the communication performance. However, most of the current prediction methods with the assumption of slowly varying channel components cannot cope with rapid variation. In this paper, we propose a novel channel prediction scheme to incorporate the mobility of both transceivers and scatterers. Based on the estimated mobility parameters, the component-wise channel extraction results are derived and then the channel frequency response is predicted. In addition, the prediction performance and the computational complexity of the proposed scheme are analyzed and evaluated, and the relationship between the mobility parameter estimation error and the channel component prediction accuracy is revealed as well. Through predicted results based on the simulated and measured data, the performance of the proposed scheme surpasses the conventional schemes with genie-aided information.

The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-based AI inference tasks in the mobile edge computing (MEC) system. We use multiply-and-accumulate operations (MACs) for problem analysis and optimize delay and energy consumption under accuracy constraints. To solve this problem, we first assume that offloading policy is known and decouple the problem into two subproblems. After solving these two subproblems, we propose an iterative-based scheduling algorithm to obtain the optimal offloading policy. We also experimentally discuss the relationship between delay, energy consumption, and inference accuracy.

This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.

As interference becomes one of the key factors restricting the performance of wireless network, many interference cancellation schemes are studied. However, most of these schemes have disadvantages in one way or another, such as high complexity and cost of the accurate feedback. In this case, blind interference cancellation schemes are proposed, which can eliminate the interference according to the received signal without any prior information, but with a very high searching complexity. To solve above issues, we propose a semi-blind interference parameter detection (Semi-BIPD) and signal restoration scheme in this paper. Firstly, a semi-blind interference detection module is investigated to detect the parameters related to strong inter-ference with the help of the received demodulation reference signal (DM-RS) sequence. Then, the channel parameters of both target user and interfering users are estimated. Finally, the signal restoration based on Semi-BIPD is conducted in the data sequence to eliminate the interference. Simulation results demonstrate that the proposed scheme can achieve better mean square error (MSE) with a low searching complexity in 5G uplink orthogonal frequency division multiplexing (OFDM) systems.

Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utmzed for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices’ SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices’ SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum efficiency after convergence.

The increasing demand for road freight has raised tremendous attention to vehicle platooning, which reduces air resistance and improves fuel economy. To achieve a small and safe spacing between vehicles while ensuring platoon stability, wireless communication assistance is indispensable. However, the imperfection of communication brings degradation of platooning performance (i.e., spacing error) and more possibilities for adversarial attacks. This article proposes a prediction-assisted platooning mechanism from the perspective of performance optimization, wherein each vehicle establishes its local platoon model based on the information received from the communication network, thereby reducing information latency. Then, to secure the system against malicious vehicles, we carry out analysis and design of a detection algorithm for a typical attack type, i.e., the data-injection attack. The detection is based on three indicators: 1) absolute spacing error; 2) spacing prediction error; and 3) acceleration prediction error. The advantages of the novel platooning mechanism and detection algorithm are ultimately demonstrated on a road-traffic simulation platform that considers the imperfection of realistic vehicle perceptions and Cellular-Vehicle-to-Everything (C-V2X) communication.

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of$[\eta, 1/\eta]$. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks.

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.

With the rising demand for indoor localization, high precision technique-based fingerprints became increasingly important nowadays. The newest advanced localization system makes effort to improve localization accuracy in the time or frequency domain, for example, the UWB localization technique can achieve centimeter-level accuracy but have a high cost. Therefore, we present a spatial domain extension-based scheme with low cost and verify the effectiveness of antennas extension in localization accuracy. In this paper, we achieve sub-meter level localization accuracy using a single AP by extending three radio links of the modified laptops to more antennas. Moreover, the experimental results show that the localization performance is superior as the number of antennas increases with the help of spatial domain extension and angular domain assisted.

Precise indoor localization is one of the key requirements for fifth Generation (5G) and beyond, concerning various wireless communication systems, whose applications span different vertical sectors. Although many highly accurate methods based on signal fingerprints have been lately proposed for localization, their vast majority faces the problem of degrading performance when deployed in indoor systems, where the propagation environment changes rapidly. In order to address this issue, the crowdsourcing approach has been adopted, according to which the fingerprints are frequently updated in the respective database via user reporting. However, the late crowdsourcing techniques require precise indoor floor plans and fail to provide satisfactory accuracy. In this paper, we propose a low-complexity self-calibrating indoor crowdsourcing localization system that combines historical with frequently updated fingerprints for high precision user positioning. We present a multi-kernel transfer learning approach which exploits the inner relationship between the original and updated channel measurements. Our indoor laboratory experimental results with the proposed approach and using Nexus 5 smartphones at 2.4GHz with 20MHz bandwidth have shown the feasibility of about one meter level accuracy with a reasonable fingerprint update overhead.

Diverse application scenarios in 5G and beyond wireless communication systems have introduced various requirements in code lengths and rates of channel codes. For the decoding of polar codes, especially the belief-propagation (BP) decoding, flexible configuration of codeword length is still not involved in current decoders. In this paper, a semi-folded decoding structure is proposed which can be reconfigured to support multiple codeword lengths. Up to 16 codes can be decoded in parallel and the utilization of processing units is no less than 87.5% for various codeword lengths. The peak throughput of 19.29 Gbps can be achieved by the proposed decoder in SMIC 55 nm CMOS technology.

With the rapid development of indoor location-based services (LBSs), the demand for accurate localization keeps growing as well. To meet this demand, we propose an indoor localization algorithm based on graph convolutional network (GCN). We first model access points (APs) and the relationships between them as a graph, and utilize received signal strength indication (RSSI) to make up fingerprints. Then the graph and the fingerprint will be put into GCN for feature extraction, and get classification by multilayer perceptron (MLP). In the end, experiments are performed under a 2D scenario and 3D scenario with floor prediction. In the 2D scenario, the mean distance error of GCN-based method is 11m, which improves by 7m and 13m compare with DNN-based and CNN-based schemes respectively. In the 3D scenario, the accuracy of predicting buildings and floors are up to 99.73% and 93.43% respectively. Moreover, in the case of predicting floors and buildings correctly, the mean distance error is 13m, which outperforms DNN-based and CNN-based schemes, whose mean distance errors are 34m and 26m respectively.

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The classification performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose a convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. We investigated the classification performance when using different attention scaling function and applying different layers. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and our method achieved the state-of-the-art or competitive classification accuracy with lower computational complexity. We also visualized our attention results and observed that the proposed attention mechanism was able to lead the network tofocus on the semantically relevant parts of environmental sounds.

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future.

In a wireless network that conveys status updates from sources (i.e., sensors) to destinations, one of the key issues studied by existing literature is how to design an optimal source sampling strategy on account of the communication constraints which are often modeled as queues. In this paper, an alternative perspective is presented—a novel status-aware communication scheme, namelyparallel communications, is proposed which allows sensors to be communication-agnostic. Specifically, the proposed scheme can determine, based on an online prediction functionality, whether a status packet is worth transmitting considering both the network condition and status prediction, such that sensors can generate status packets without communication constraints. We evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform, which is integrated with a collaborative autonomous driving simulator, i.e., Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control models and road conditions. The results show that with online status predictions, the channel occupancy is significantly reduced, while guaranteeing low status recovery error. Then the framework is applied to two scenarios: a multi-density platooning scenario, and a flight formation control scenario. Simulation results show that the scheme achieves better performance on the network level, in terms of keeping the minimum safe distance in both vehicle platooning and flight control.

Real-time status update in future vehicular networks is vital to enable control-level cooperative autonomous driving. Cellular Vehicle-to-Everything (C-V2X), as one of the most promising vehicular wireless technologies, adopts a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol for its sidelink communications. Despite the recent and ongoing efforts to optimize SPS, very few work has considered the status update performance of SPS. In this paper, Age of Information (AoI) is first leveraged to evaluate the MAC layer performance of C-V2X sidelink. Critical issues of SPS, i.e., persistent packet collisions and Half-Duplex (HD) effects, are identified to hinder its AoI performance. Therefore, a piggyback-based collaboration method is proposed accordingly, whereby vehicles collaborate to inform each other of potential collisions and collectively afford HD errors, while entailing only a small signaling overhead. Closed-form AoI performance is derived for the proposed scheme, optimal configurations for key parameters are hence calculated, and the convergence property is proved for decentralized implementation. Simulation results show that compared with the standardized SPS and its state-of-the-art enhancement schemes, the proposed scheme shows significantly better performance, not only in terms of AoI, but also of conventional metrics such as transmission reliability.

The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time.

In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions. Conventional methods are mostly based on classical control theory, e.g., Kalman filter and its variations, which mainly deal with stationary scenarios. Therefore, severe application limitations exist, especially with complicated, dynamic Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this subject, by first modifying the classical approaches, e.g., Extended Kalman Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then proposing a Reinforcement Learning (RL)-based approach that can achieve the URLLC requirements in a typical intersection scenario. Simulation results based on a commercial ray-tracing simulator show that enhanced EKF and PF methods achieve packet delay more than 10 ms, whereas the proposed deep RL-based method can reduce the latency to about 6 ms, by extracting context information from the training data.

Face Anti-spoofing (FAS) is a challenging problem due to the complex serving scenario and diverse face presentation attack patterns. Using single modal images which are usually captured with RGB cameras is not able to deal with the former because of serious overfitting problems. The existing multi-modal FAS datasets rarely pay attention to the cross domain problems, training FAS system on these data leads to inconsistencies and low generalization capabilities in deployment since imaging principles(structured light, TOF, etc.) and pre-processing methods vary between devices. We explore the subtle fine-grained differences betweeen multi-modal cameras and proposed a cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community. Furthermore, we incorporate the multiplicative attention and center loss to enhance the representative power of CNN via seeking out complementary information as a powerful baseline. In addition, extensive experiments have been conducted on the proposed dataset to analyze the robustness to distinguish spoof faces and bona-fide faces. Experimental results show the effectiveness of proposed method and achieve the state-of-the-art competitive results. Finally, we visualize our future distribution in hidden space and observe that the proposed method is able to lead the network to generate a large margin for face anti-spoofing task.

Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes-this demonstrates the usefulness of our proposed status update schemes in a real-world application.

Synchronization, as a critical factor of modern wireless communication systems, has attracted close research attention recent years. For the vehicle-to-anything (V2X) communication in 5G new radio, synchronization faces severe challenges due to the extremely low latency and high reliability requirements. In this paper, a terminal aided synchronization scheme is proposed for the vehicle platooning in V2X communication. The shared information, such as NSLIDfrom other cooperative vehicles, are utilized to recover the original transmitted sidelink synchronization signals. The synchronization ID detection probability is therefore improved by 49.6% compared to conventional schemes. Hardware implementation on FPGA Artix-7 AC701 board is performed of the proposed synchronization scheme and the hardware latency is reduced to 67.18 μs compared to 968,654.85 μs in conventional schemes.

With the development of deep learning, face recognition is attracting more and more attention in both industry and academia. Hardware implementation of face recognition systems on heterogeneous embedded devices, however has been rarely studies. In this paper, an embedded face recognition system is designed and implemented on FPGA SoC platforms. A hardware-software partition method is first introduced by analyzing the ratio between computation and memory access of critical tasks in the system. Several acceleration methods are then exploited to optimize the hardware implementation. The face recognition system is implemented on Xilinx FPGA MPSoC ZCU102 with 97.3% recognition accuracy and 203.7 ms latency. The neural network VIPLFace, as the most time consuming part of the system, has a 74 ms latency, 71× faster after hardware-software co-design.

Vehicular communications, considered as one of the key technologies for autonomous driving, are still faced with significant challenges, e.g., distributed resource allocation in the direct communication mode. In Cellular Vehicle-to-Everything (C-V2X), a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol is adopted by 3rd Generation Partnership Project (3GPP) for its sidelink communications. However, the performance of either the SPS scheme in standard or the state-of-the-art enhancement works is not satisfactory. In this paper, we propose a piggyback-based collaboration method and firstly introduce Age of Information (AoI) to evaluate the status update performance of C-V2X. In addition, the closed-form AoI performance is obtained. Through extensive simulations, the proposed scheme exhibits better performance than the current schemes, both in terms of AoI and transmission reliability.

Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload in heterogeneous networks. Prior works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a composite framework consists of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and users respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviors of users. A new algorithm based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q- network (DQN) algorithm are respectively applied to users and SBS due to different complexity of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables users' devices and SBS to prefetch the optimal files while learning, and hence reducing the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

Channel estimation is crucial for modern WiFi system and becomes more and more challenging with the growth of user throughput in multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms without pre-assumed channel characteristics in this paper. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks, and directly apply them in some practical WiFi prototype systems. As shown in this paper, the proposed super-resolution based channel estimation scheme can outperform the conventional approaches in both LOS and NLOS scenarios, which we believe can significantly change the current channel estimation method in the near future.

Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems.

In this paper, a pre-RTL neural network simulator (SimuNN) is proposed which is initiated as the bridge between the algorithm design and hardware implementation of neural networks. SimuNN is compatible with TensorFlow interface, and supports inference in both floating-point numbers and configurable fixed-point numbers. It can provide inference results at layer-/module-/cycle-level to serve as a golden model for RTL designs. Besides, its embedded model for hardware performance estimation enables SimuNN to provide an accurate reference of processing speed and hardware cost at the ASIC-designed user end for algorithm designers.

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms.

Although almost blank subframes (ABS) proposed in heterogeneous networks (HetNet) can enhance the performance of user equipments (UEs) in Pico-cell range expansion (CRE) area, it also significantly degrades the Macro-cell throughput. To address this issue, further-enhanced inter-cell interference coordination (FeICIC) scheme is considered in 3GPP Release 11, where low power ABS (LP-ABS) are adopted for the Macro-cell center region users to improve the Macro-cell throughput. However, LP-ABS power, Pico CRE bias and Pico base station (PBS) density will jointly affect on the system performance, which eventually deteriorates the network energy efficiency (EE) without careful configuration. In this paper, we first deduce the closed-form expression of network EE as a function of PBS density, Pico CRE bias and LP-ABS power based on stochastic geometry model. Then we provide Monte Carlo simulations to verify the accuracy of theoretical derivation of the network EE and analyze the impacts of these parameters on the network EE. The simulation results show that the reasonable PBS density, Pico CRE bias and LP-ABS power can improve the network EE obviously.

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.

Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.

In vehicular communications, intracell interference and the stringent latency requirement are challenging issues. In this paper, a joint spectrum reuse and power allocation problem is formulated for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Recognizing the high capacity and low-latency requirements for V2I and V2V links, respectively, we aim to maximize the weighted sum of the capacities and latency requirement. By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain real-time decisions on spectrum reuse and power allocation. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 3.62% of its CPU runtime.

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

With the raising demand for autonomous driving, vehicle-to-vehicle communications becomes a key technology enabler for the future intelligent transportation system. Based on our current knowledge field, there is limited network simulator that can support end-to-end performance evaluation for LTE-V based vehicle-to-vehicle platooning systems. To address this problem, we start with an integrated platform that combines traffic generator and network simulator together, and build the V2V transmission capability according to LTE-V specification. On top of that, we simulate the end-to-end throughput and delay profiles in different layers to compare different configurations of platooning systems. Through numerical experiments, we show that the LTE-V system is unable to support the highest degree of automation under shadowing effects in the vehicle platooning scenarios, which requires ultra-reliable low-latency communication enhancement in 5G networks. Meanwhile, the throughput and delay performance for vehicle platooning changes dramatically in PDCP layers, where we believe further improvements are necessary.

The rapid increasing demand of wireless transmission has incurred mobile broadband to continuously evolve through multiple frequency bands, massive antennas and other multi-stream processing schemes. Together with the improved data transmission rate, the power consumption for multi-carrier transmission and processing is proportionally increasing, which contradicts with the energy efficiency requirements of 5G wireless systems. To meet this challenge, multi carrier power amplifier (MCPA) technology, e.g., to support multiple carriers through a single power amplifier, is widely deployed in practical. With massive carriers required for 5G communication and limited number of carriers supported per MCPA, a natural question to ask is how to map those carriers into multiple MCPAs and whether we shall dynamically adjust this mapping relation. In this paper, we have theoretically formulated the dynamic carrier and MCPA mapping problem to jointly optimize the traditional separated baseband and radio frequency processing. On top of that, we have also proposed a low complexity algorithm that can achieve most of the power saving with affordable computational time, if compared with the optimal exhaustive search based algorithm.

To improve the spectrum efficiency of high-order modulation polar codes, a novel modulation strategy, namely displacement of balanced modulation (DBM) is proposed in this paper. This algorithm can balance the performance between bit levels under the universal encoder and decoder by adding a shift mapping matrix. Simulation results show that the presented DBM algorithm can achieve almost the same performance as the multilevel coding (MLC) technique with more flexibility. To further reduce the computational complexity of the proposed DBM algorithm, a simplified algorithm called reduced-complexity DBM (RC-DBM) is also presented, where location-based reliability sorting construction with complexity O(n) instead of Gaussian approximation (GA) construction with complexity O(N logN) is used, and the computing process of DBM is replaced with the structure characteristics of polar codes. Simulation results show that the proposed RC-DBM algorithm has a tolerable performance loss compared to that of DBM.

This paper studies the energy efficiency (EE) maximization for an orthogonal frequency division multiple access (OFDMA) downlink network aided by a relay station (RS) with subcarrier pairing. A highly flexible transmission protocol is considered, where each transmission is executed in two time slots. Every subcarrier in each slot can either be used in direct mode or be paired with a subcarrier in another slot to operate in relay mode. The resource allocation (RA) in such a network is highly complicated, because it has to determine the operation mode of subcarriers, the assignment of subcarriers to users, and the power allocation of the base station and RS. We first propose a mathematical description of the RA strategy. Then, a RA algorithm is derived to find the globally optimum RA to maximize the EE. Finally, we present extensive numerical results to show the impact of minimum required rate of the network, the user number, and the relay position on the maximum EE of the network.

Path metric sorting unit of successive cancellation list (SCL) decoders for polar codes is the main concern in this paper. After reviewing existing sorting units in SCL decoders, we propose 2 new sorting schemes namely quick select (QS) based selection algorithm and simplified bitonic sorter (SBT), which exploit the special data dependency of path metrics in log-likelihood ratio based SCL decoding. Theoretical analysis shows that for the list size of L ≤ 8, QS-based selection algorithm has lower delay than existing schemes. FPGA implementation based on Artix7 Family shows that for the list size of L ≥ 16, SBT has the same delay while the hardware reduction is over 40%.

In this paper, the successive cancellation (SC) heap polar decoding scheme is firstly proposed to reduce the complexity. Unlike SC list decoder which keepsLsame length paths, SC heap decoding stores different length paths in a heap and always decodes the global optimal path in the root. It has been strictly proved that SC heap decoding is superior to SC stack decoding because the time complexity of inserting new paths is only related to the height of the heap. Proposed SC heap decoding is a dynamic decoding scheme with robustness in various scenarios. Numerical results with binary-input additive white Gaussian noise channel (BI-AWGNC) show that SC heap decoding reduces 63.53% decoding complexity compared with SC list decoding on the same performance at SNR of 2.5 dB. A low-complexity hardware architecture for proposed SC heap decoder is also designed.

Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme, in which multiple users access the same channel with user-specific sparse codewords. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver which fully utilizes the diversity gain and coding gain in the system. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one, and the performance gain increases with the system load. It is also shown that SCMA can work well in highly overloaded scenario, and the link-level performance does not degrade even if the load is as high as 300%.

The rapid traffic growth and ubiquitous access requirements make it essential to explore the next generation (5G) wireless communication networks. In the current 5G research area, non-orthogonal multiple access has been proposed as a paradigm shift of physical layer technologies. Among all the existing non-orthogonal technologies, the recently proposed sparse code multiple access (SCMA) scheme is shown to achieve a better link level performance. In this paper, we extend the study by proposing an unified framework to analyze the energy efficiency of SCMA scheme and a low complexity decoding algorithm which is critical for prototyping. We show through simulation and prototype measurement results that SCMA scheme provides extra multiple access capability with reasonable complexity and energy consumption, and hence, can be regarded as an energy efficient approach for 5G wireless communication systems.

In this paper, we consider energy efficient coverage planning in cellular networks. To save energy, each base station (BS) can work in sleep mode when there is no user in its coverage or the users can be served by neighbor base stations. With increasing coverage overlap, there is a tradeoff between the number of BSs per unit area and the proportion of active base stations. Analytical and numerical methods are presented to evaluate coverage planning schemes with different inter-BS distance. Evaluation results show that compared with the minimum coverage overlap scheme, the optimal planning scheme can reduce the network energy consumption by more than 20%, and the performance improvement depends on the user density, network topology, and the power consumption of sleep mode.

To meet the global challenge of reducing greenhouse gas emissions and the explosive demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is spectrum-efficient as well as energy-efficient. In this paper, we characterize the spectrum efficiency (SE) and energy efficiency (EE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that SE and EE may not be contradictory to each other as in the traditional networks. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

Bandwidth-power tradeoff, as one of the key relations in the green radio research framework, has attracted numerous research attentions in the recent years, due to the refreshed spectrum policies and the popularity of enabling techniques for bandwidth adjustment (such as software defined radio). Previous literatures show some preliminary results on the bandwidth-power tradeoff for heterogeneous networks, however, the practical issues such as site sleeping and inter-cell interference are still out of the research scope. In this paper, we formulate the network energy minimization problem and investigate the optimal bandwidth allocation strategy under the above two issues. Optimal bandwidth-power tradeoff is then derived to show the effects of key system parameters, including cell sizes and interference power levels, followed by some numerical examples to co-verify the analytical results.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the ergodic capacities from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. A three-step searching algorithm is developed to solve this problem. We then analyze the impact of spatial-frequency resources, overall SE requirement and user fairness on the SE-EE relationship. Analytical and simulation results show that increasing frequency resource is more efficient than increasing spatial resource to improve the SE-EE relationship as a whole. The EE increases with the SE when the frequency resource is not constrained to the maximum value, otherwise a tradeoff between the SE and the EE exists. Sacrificing the fairness among users in terms of ergodic capacities can enhance the SE-EE relationship. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial or frequency resource.

To meet the global challenge of reducing greenhouse gas emissions and the explosion demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is cost-effective as well as energy-efficient. In this paper, we characterize the energy efficiency (EE) and deployment efficiency (DE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that a proper density of small cells is required to obtain the maximum achievable EE/DE. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the average data rates from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. We develop a two-step searching algorithm to solve this problem, which first finds the near-optimal numbers of subcarriers for multiple users based on Karush-Kuhn-Tucker (KKT) conditions and then optimize the number of active RF chains. Simulation results demonstrate that increasing frequency resource improves both the SE and the EE, and is more efficient than increasing spatial resource. Consequently, there exists tradeoff between the SE and the EE only when the frequency resource is limited. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial resource and that of only frequency resource.

In this paper, we focus on the power-delay tradeoff for downlink transmission systems. In particular, we target to design the adaptive modulation scheme that achieves the optimal power-delay tradeoff under practical concerns, such as the practical power consumption model of a base station and the dynamics of user traffic. The optimal modulation level selection problem is formulated in a Markov decision problem and a cross-layer approach is adopted to take both queue and channel dynamics into consideration. Simulation results show that the proposed policy achieves about 20% power saving for the same delay performance, compared with fixed modulation level schemes. In addition, the impact of the practical power consumption model under dynamic traffic does change the optimal modulation level selection philosophy.

One of the main tasks for future wireless systems is to reduce the environmental impacts of the wireless transmission under different network architectures, which motivates the research on the green radio. Traditional research focuses on study of the tradeoff relation in the homogeneous network architecture and the extension to the heterogeneous network is still open in the literature. In this paper, we shall investigate this open issue and derive the closed-form expression for the optimal bandwidth allocation scheme as well as the bandwidth-power tradeoff relation. The analytical result is then extended to the practical settings with the network layout of 19 hexagonal macro-cells. Both the analytical and numerical results show that the proposed bandwidth allocation scheme achieves the best bandwidth-power tradeoff in the heterogeneous network architecture, which is of great importance for the heterogeneous network deployment and the frequency planning. Moreover, we also show that the radius of the network and the power budget play an important role in the bandwidth-power tradeoff relation, which should be carefully considered in the heterogeneous network planning and optimization.

The explosively increasing demand of high-data-rate multimedia wireless services and ubiquitous access has triggered rapidly booming energy consumption at the wireless network operator side. Therefore, energy-efficient design is becoming a mainstream for future wireless networks. In this paper, we study energy-efficient resource allocation in downlink OFDMA networks with partial channel state information at the transmitter (CSIT). To reduce the channel state information (CSI) feedback overhead while maintaining relatively high achievable energy efficiency (EE), we propose a novel CSI feedback scheme, which leads to higher EE with lower feedback overhead compared with the conventional selective feedback (SF) and bit-map based feedback (BF) schemes. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our CSI feedback scheme outperforms the conventional CSI feedback schemes in EE and spectral efficiency (SE) with almost the same feedback overhead.

Optimizing energy efficiency (EE) for the MIMO broadcasting channels (BC) is considered in this paper, where a practical power model is taken into account. Although the EE of the MIMO BC is non-concave, we reformulate it as a quasiconcave function based on the uplink-downlink duality. After that, an energy efficient iterative waterfilling scheme is proposed based on the block-coordinate ascent algorithm to obtain the optimal transmission policy efficiently, and the solution is proved to be convergent. Through simulations, we validate the efficiency of the proposed scheme and discuss the system parameters' effect on the EE.

The widespread application of multimedia wireless services and requirements of ubiquitous access have triggered rapidly booming energy consumption at both the base station side and the user equipment (UE) side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study the energy-efficient resource allocation in both downlink and uplink cellular networks with orthogonal frequency division multiple access (OFDMA). For the downlink transmission, the generalized energy efficiency (EE) is maximized while for the uplink case the minimum individual EE is maximized, both under certain prescribed per-UE quality-of-service (QoS) requirements. For both transmission scenarios, we first provide the optimal solution and then develop a suboptimal but low-complexity approach by exploring the inherent structure and property of the energy-efficient design. For the downlink case, by modifying the original problem, we also find a computationally efficient and numerically tractable upper bound on the EE, which indicates the performance limit and is demonstrated to be quite tight if the number of subcarriers is larger than that of UEs and motivates us to find a near-optimal approach relying on the quasiconcave relation between the modified EE and transmit power. Simulation results show that the energy-efficient design greatly improves EE compared with the conventional spectral-efficient design and the low-complexity suboptimal approaches can achieve a promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss.

In this paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalentrate constraint approach, the Lyapunov stability drift approach, and the approximate Markov decision process approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity, and implementation issues. For each of the approaches, the problem setup, the general solution, and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multihop routing designs in general multihop networks are elaborated. Finally, the delay performances of various approaches are compared through simulations using an example of the uplink OFDMA systems.

Cooperative communication is an important technology in next generation wireless networks. Aside from conventional amplify-and-forward (AF) and decode-and-forward (DF) protocols, the partial decode-and-forward (PDF) protocol is an alternative relaying scheme that is especially promising for scenarios in which the relay node cannot reliably decode the complete source message. However, there are several important issues to be addressed regarding the application of PDF protocols. In this paper, we propose a PDF protocol and MIMO precoder designs at the source and relay nodes. The precoder designs are adapted based on statistical channel state information for correlated MIMO channels, and matched to practical minimum mean-square-error successive interference cancelation (MMSE-SIC) receivers at the relay and destination nodes. We show that under similar settings, the proposed MIMO precoder design with PDF protocol and MMSE-SIC receivers achieves substantial performance enhancement compared with conventional baselines.

The widespread application of multimedia wireless services and requirement of ubiquitous access have triggered rapidly booming energy consumption at both the base station side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study energy-efficient resource allocation in downlink cellular OFDMA networks. For the downlink transmission, the weighted energy efficiency (EE) is maximized under certain prescribed per-user quality- of-service (QoS) requirements. We first obtain the optimal solution then propose a suboptimal approach by exploring the inherent structure and property of the energy-efficient design to reduce complexity. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our low- complexity suboptimal approaches can achieve promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated aiming at maximizing energy efficiency(EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function when the channel estimation error is considered, which depends on the large-scale channel gains of multiple users, the allocated power to pilots and data symbols, and the circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. The relationship between the power for pilots and data symbols is analyzed based on Karush-Kuhn-Tucker (KKT) conditions and the impacts of channel gains on both power allocation and the EE are studied. Exploiting the quasiconcavity property of the EE function, a bisection searching algorithm is developed to find the optimal power allocation. Simulation results demonstrate the performance gain of the proposed optimal power allocation scheme in terms of the EE and the required overall transmit power.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design in wireless networks is becoming more and more important. In this paper, the fundamental tradeoff relation between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We obtain a tight upper bound and lower bound on the optimal EE-SE tradeoff relation for general scenarios based on Lagrange dual decomposition, which accurately reflects the optimal EE-SE tradeoff relation. We then focus on a special case that priority and fairness are considered and derive an alternative upper bound, which is even proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of EE-SE tradeoff. Numerical results demonstrate that the optimal EE-SE tradeoff relation is a bell shape curve and can be well approached with our resource allocation algorithm.

Energy efficiency has become an important metric for the future system design. In traditional design methods, the link transmission strategies were investigated with only transmit power considered. We shall show in this paper that from the whole system's aspect, the radiated energy used for data transmission is only a portion of the overall power consumption, whose ratio depends on many practical issues such as the transmission distance, the modulation level, the non-ideal power amplifier efficiency, as well as the circuit and processing power. Through closed-form formula derivation, analysis remarks, and numerical examples, we shall show the impact of different system parameters and configurations on the whole system's power consumption and energy efficiency, which in turn, sheds a light on the design philosophy for maximizing the energy efficiency of a base station as a whole.

In this paper, both configuration of active radio frequency (RF) chains and resource allocation are investigated for improving energy efficiency of downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We first formulate an optimization problem to minimize the total power consumed at the base station with the maximum transmit power constraint and ergodic capacity constraints from multiple users. Then a two-step suboptimal algorithm is proposed. Specifically, the continuous variable optimization problem is first solved, and then a discretization algorithm is presented to obtain the number of active RF chains and the number of subcarriers allocated to each user. Simulation results demonstrate that the proposed algorithm can provide significant power-saving gain over the all-on RF chain scheme and the adaptive subcarrier allocation helps to save more power.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

In this paper, we consider an open-loop network MIMO system with K BSs serving K private MSs and Mccommon MS based on a novel partial cooperation overlaying scheme. Exploiting the heterogeneous path gains between the private MSs and the common MSs, each of the K BSs serves a private MS non-cooperatively and the K BSs also serve the Mccommon MSs cooperatively. The proposed scheme does not require closed loop instantaneous channel state information feedback, which is highly desirable for high mobility users. Furthermore, we formulate the long-term distributive power allocation problem between the private MSs and the common MSs at each of the K BSs using a partial cooperative game. We show that the long-term power allocation game has a unique Nash Equilibrium (NE) but standard best response update may not always converge to the NE. As a result, we propose a low-complexity distributive long-term power allocation algorithm which only relies on the local long-term channel statistics and has provable convergence property.

In this paper, we consider the problem of multi-relay selection for multi-stream cooperative MIMO systems with M relay nodes. Traditionally, relay selection approaches are primarily focused on selecting one relay node to improve the transmission reliability given a single-antenna destination node. As such, in the cooperative phase whereby both the source and the selected relay nodes transmit to the destination node, it is only feasible to exploit cooperative spatial diversity (for example by means of distributed space time coding). For wireless systems with a multi-antenna destination node, in the cooperative phase it is possible to opportunistically transmit multiple data streams to the destination node by utilizing multiple relay nodes. Therefore, we propose a low overhead multi-relay selection protocol to support multi-stream cooperative communications. In addition, we derive the asymptotic performance results at high SNR for the proposed scheme and discuss the diversity-multiplexing tradeoff as well as the throughput-reliability tradeoff. From these results, we show that the proposed multi-stream cooperative communication scheme achieves lower outage probability compared to existing baseline schemes.

The pressure of the energy bill from operators and the request of the environmental protection from the governments and publics results in more urgent requirement on the energy consumption and CO2emission. Traditionally, adaptive modulation has been proven to be an efficient solution to improve the system performance over the radio fading channels. To obtain the design insights for energy efficient adaptive modulation scheme, we shall introduce a circuit-level power modeling to analyze the effects and characterize the bandwidth-power-energy efficiency tradeoff relations for wireless communication systems. We show that different bandwidth, power and modulation schemes shall be used under different channel conditions to maximize the energy efficiency and the adaptive modulation scheme can greatly help to improve the tradeoff curves.

In this paper, we quantify the benefits of employing relay station in large-coverage cognitive radio systems which opportunistically access the licensed spectrum of some small-coverage primary systems scattered inside. Through analytical study, we show that even a simple decode-and-forward (SDF) relay, which can hold only one packet, offers significant pathloss gain in terms of the spatial transmission opportunities and link reliability. However, such scheme fails to capture the spatial-temporal burstiness of the primary activities, that is, when either the source-relay (SR) link or relay-destination (RD) link is blocked by the primary activities, the cognitive spectrum access has to stop. To overcome this obstacle, we further propose buffered decode-and-forward (BDF) protocol. By exploiting the infinitely long buffer at the relay, the blockage time on either SR or RD link is saved for cognitive spectrum access. The buffer gain is shown analytically to improve the stability region and average end-to-end delay performance of the cognitive relay system.

Cooperative communication is an important technology in next generation wireless networks. Partial decode-and-forward provides an alternative solution for the conventional amplify-and-forward as well as decode-and-forward relay protocols. However, there are several important issues to be addressed regarding the application of partial decode-and-forward protocol. In this paper, we address the practical issues by proposing a joint MIMO precoder design for the multi-antenna cooperative system with correlated MIMO fading and partial decode-and-forward relay protocol. In addition, the MIMO precoders at both the source and the relay are matched to the MMSE-SIC receivers at the destination. We find that under similar system settings, joint MIMO precoder design with partial decode-and-forward relay protocol and MMSE-SIC receivers achieves substantial performance enhancement and has important practical significance.

Cognitive and cooperative technologies are two core components in the design of next generation wireless networks. One key issue associated with cognitive transmission is the inefficient spectrum sharing of the secondary system, especially for secondary communications separated by long distance. To boost the spectrum sharing efficiency, cognitive multi-relay system appears to be an attractive solution for the cognitive transmission systems. In this paper, we consider a cognitive multi-relay (CMR) system and propose a novel CMR buffered decode-and-forward protocol that exploit the buffers in the source and each relay node. Moreover, we derive the closed-form average end-to-end delay and the stability region by exploiting the birth-death nature of the queue dynamics and the methods of state aggregation and queue dominance. Comparing with the baseline protocols through analytical and numerical results, the proposed CMR-BDF scheme can dynamically adjust the cognitive transmission to exploit the spatial PU burstiness while simultaneously benefits from the advantage of double-sided selection diversity in both the source-relay and relay-destination interfaces.

In this paper, we shall focus on the design and asymptotic performance analysis of joint source channel coding (JSCC) for MIMO channels with limited Channel State Information (CSI) feedback. We consider rate adaptation, spatial power adaptation as well as precoder adaptation. We find that the optimal distortion exponent (achieved with perfect CSIT) can be realized using only spatial power adaptation and precoder adaptation with sufficiently large feedback rate. We also compare the average distortion of the limited feedback JSCC system using fixed bandwidth expansion scheme and dynamic bandwidth expansion scheme. We show that dynamic bandwidth expansion is more effective to reduce the limited feedback JSCC performance especially at large average bandwidth expansion.

To significantly enhance the spectral efficiency of the MBS service, UEP and MIMO are important core technologies to realize such challenging goal. Taking into account of the implementation constraints, 4 times 2 is widely accepted as a reasonable MIMO setup and is a mandatory configuration of the next generation wireless systems. Within the 4 times 2 MIMO configuration, D-STTD is an important transmission scheme which strikes a balance between spatial diversity and spatial multiplexing and it can achieve better performance than VBLAST. Traditional approaches focus on different ways to introduce UEP in single antenna systems. However, it is difficult to apply the conventional schemes in the D-STTD systems since the decoding error is mainly contributed by the spatial interference between different streams. In this paper, we shall propose a novel UEP scheme for MBS services using D-STTD in 4times2 MIMO systems and formulate a general design framework to determine various parameters of the proposed UEP scheme. For illustration, we give two examples on the determination of the UEP parameters for the uncoded UEP design and the coded UEP design. From the results, we found that the proposed UEP scheme can achieve significant MBS system performance gain compared to those baseline systems.

Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum. Conventional distributed sensing and centralized decision framework involving multiple sensor nodes is proposed to enhance the sensing performance. However, it is difficult to apply the conventional schemes in reality since the overhead in sensing measurement and sensing reporting as well as in sensing report combining limit the number of sensor nodes that can participate in distributive sensing. In this paper, we shall propose a novel, low overhead and low complexity energy detection based cooperative sensing framework for the cognitive radio systems which addresses the above two issues. The energy detection based cooperative sensing scheme greatly reduces the quiet period overhead (for sensing measurement) as well as sensing reporting overhead of the secondary systems and the power scheduling algorithm dynamically allocate the transmission power of the cooperative sensor nodes based on the channel statistics of the links to the BS as well as the quality of the sensing measurement. In order to obtain design insights, we also derive the asymptotic sensing performance of the proposed cooperative sensing framework based on the mobility model. We show that the false alarm and mis-detection performance of the proposed cooperative sensing framework improve as we increase the number of cooperative sensor nodes.

This paper considers the relay selection problem for the cooperative spatial multiplexing scheme in wireless systems with M relay nodes. We consider half-duplex relays in which the relay nodes cannot transmit and receive simultaneously on the same frequency. To improve the transmission reliability, traditional approaches employ cooperative spatial diversity (such as distributed space-time coding) during the cooperative phase when relay and source both transmit to the destination. Most of the relay selection schemes considered in the literature deal with selecting one relay only. However, when we consider wireless systems with multi-antenna destination, we may have opportunities to transmit extra data through cooperative spatial multiplexing by selecting multiple relays to participate in the cooperative phase. In this paper, we shall propose a low complexity multi- relay selection algorithm for the cooperative spatial multiplexing using decode-and-forward (DF) protocol. We have also analyzed the asymptotic performance as well as the diversity-multiplexing tradeoff (DMT) of the proposed system. We found that the proposed cooperative spatial multiplexing scheme with multi- relay selection achieves a much better DMT tradeoff than the traditional cooperative diversity schemes.

This paper considers the resource allocation problem in the wireless OFDMA system with a relay node. We consider orthogonal relay in which the relay node cannot transmit and receive simultaneously on the same frequency. As a result, the use of relay node may enhance or degrade the achievable transmission rate depending on the instantaneous channel states between the source and the relay, the relay and the destination as well as the source and the destination. Hence, it is very important to dynamically adjust the resources (subbands) allocated to the relay node so that the relay is used only at the right time according to the instantaneous channel states. Conventional approaches dynamically schedule the usage of the relay node in a centralized manner in which full knowledge of the channel states between any two nodes in the network is required. However, perfect knowledge of the channel states at various nodes is very difficult to obtain. In this paper, we shall propose a resource allocation algorithm, which iteratively allocates resource to the source and relay, and converge to the close-to-optimal allocation. Asymptotic achievable rate of the proposed algorithm is derived. We show that the system achieves significant improvement of the achievable rate compared to the point-to-point baseline system without relay as well as the baseline system with random subband allocation.

This paper considers the resource allocation problem in the wireless OFDMA systems with a relay node. We consider half-duplex relay in which the relay node cannot transmit and receive simultaneously on the same frequency. As a result, the use of relay node may enhance or degrade the system throughput depending on the instantaneous channel states between the source and relay, the relay and the destination as well as the source and destination. Hence, it is very important to dynamically adjust the resource (subcarrier) allocated to the relay node so that the relay is used only at the right time according to the instantaneous channel states. Conventional approaches dynamically schedule the usage of the relay node in a centralized manner in which full knowledge of the channel states between any two nodes in the network is required. However, perfect knowledge of the channel states at various nodes are very difficult to obtain. In this paper, we shall propose a distributed resource allocation algorithm on the OFDMA system with half-duplex relay by employing rateless code. Based on the ACK/NAK exchanges between the source, destination and relay, the proposed algorithm iteratively allocates resource to the source and relay, and converge to the close-to-optimal allocation within finite steps. The resource allocation algorithm has low complexity and provable convergence property. Asymptotic throughput performance of the proposed algorithm is derived. We show that the system achieves significant throughput gain compared to the point-to-point baseline system without relay as well as the baseline system with random subcarrier allocation.